[unreadable] This research will develop new statistical methods for fine-scale localization of the positions, in the human genome, of mutations that influence susceptibility to complex genetic diseases. Complex genetic diseases such as multiple sclerosis and type II diabetes are a result of the interaction of multiple genes, and the environment. Complex genetic diseases are relatively common in the population and therefore have a large socioeconomic impact and are of great importance for public health. Conventional methods for identifying mutations that cause simple genetic disorders (such as cystic fibrosis) use linkage analysis of markers transmitted to affected and unaffected relatives. These methods often have low power to detect complex disease mutations due to such factors as incomplete penetrance. A more powerful approach for identifying genes influencing complex genetic diseases uses population association studies of unrelated affected and normal individuals. Polymorphisms with detectable frequency differences between the two groups are candidate disease genes. One goal of the proposed research is to develop new statistical methods for fine-scale mapping of disease mutations using population linkage disequilibrium; these methods will be specifically designed for use in fine-mapping of genes influencing complex diseases. Another goal is to develop statistical tools to facilitate population association analyses of complex diseases. These tools include new statistical methods for inferring haplotype phase (which marker alleles are carried on which chromosomes) using new kinds of genotyping data, and new statistical methods for determining the rates of recombination among markers at a fine scale in the human genome. These new methods will together provide powerful bioinformatics tools, and computer programs, for use by researchers carrying out association studies aimed at identifying genes influencing complex diseases. [unreadable] [unreadable]